Document Type

Conference Paper

Publication Date

2015

DOI

10.1117/12.2082646

Publication Title

Image Processing: Algorithms and Systems XIII, Proceedings of SPIE-IS&T Electronic Imaging, SPIE Vol. 9399

Volume

9399

Pages

939904 (1-7)

Conference Name

Image Processing: Algorithms and Systems XIII, SPIE-IS&T Electronic Imaging, February 8-12, 2015, San Francisco, California

Abstract

Isomap is a classical manifold learning approach that preserves geodesic distance of nonlinear data sets. One of the main drawbacks of this method is that it is susceptible to leaking, where a shortcut appears between normally separated portions of a manifold. We propose an adaptive graph construction approach that is based upon the sparsity property of the ℓ1 norm. The ℓ1 enhanced graph construction method replaces k-nearest neighbors in the classical approach. The proposed algorithm is first tested on the data sets from the UCI data base repository which showed that the proposed approach performs better than the classical approach. Next, the proposed approach is applied to two image data sets and achieved improved performances over standard Isomap.

Rights

Copyright 2015 Society of Photo‑Optical Instrumentation Engineers (SPIE).

One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited.

Original Publication Citation

Tran, L. Zheng, Z., Zhou, G., & Li, J. (2015) Adaptive graph construction for Isomap manifold learning. In K.O. Eglazarian, S.S. Agaian, & A.P. Gotchev (Eds.), Image Processing: Algorithms and Systems XIII, Proceedings of SPIE-IS&T Electronic Imaging, SPIE Vol. 9399 (939904). SPIE. https://doi.org/10.1117/12.2082646

ORCID

0000-0003-0091-6986 (Li)

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